Spectral identification and classification of dusty stellar sources using spectroscopic and multiwavelength observations through machine learning

TL;DR

This study develops a machine learning framework to identify and classify dusty stellar sources using multiwavelength data, achieving high accuracy even with limited spectroscopic samples.

Contribution

The paper introduces a novel machine learning approach combining spectroscopic and photometric data for classifying dusty stellar sources, demonstrating effective performance with limited training data.

Findings

Support Vector Classifier achieved highest accuracy.

Effective classification of various stellar subcategories.

Robust models trained with limited spectroscopic data.

Abstract

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected over the IR sky. Spectroscopic data is typically used to identify specific infrared sources. However, our goal is to determine how well these sources can be identified using multiwavelength data. Consequently, we developed a robust training set of spectra of confirmed sources from the Large and Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-Spec Spitzer Infrared Spectrograph (IRS) spectral catalogs. Subsequently, we applied various learning classifiers to distinguish stellar subcategories comprising young stellar objects (YSOs), C-rich asymptotic giant branch (CAGB), O-rich AGB stars (OAGB), Red supergiant (RSG), and post-AGB stars. We have classified around 700 counts of these sources. It should be highlighted that despite utilizing the limited spectroscopic data we trained, the accuracy and models' learning curve provided outstanding results for some of the models. Therefore, the Support Vector Classifier (SVC) is the most accurate classifier for this limited dataset.